Skip to main content

MIRIX Server - Multi-Agent Personal Assistant with Advanced Memory System

Project description

Mirix Logo

MIRIX - Multi-Agent Personal Assistant with an Advanced Memory System

Your personal AI that builds memory through screen observation and natural conversation

| 🌐 Website | 📚 Documentation | 📄 Paper | 💬 Discord


Key Features 🔥

  • Multi-Agent Memory System: Six specialized memory components (Core, Episodic, Semantic, Procedural, Resource, Knowledge Vault) managed by dedicated agents
  • Screen Activity Tracking: Continuous visual data capture and intelligent consolidation into structured memories
  • Privacy-First Design: All long-term data stored locally with user-controlled privacy settings
  • Advanced Search: PostgreSQL-native BM25 full-text search with vector similarity support
  • Multi-Modal Input: Text, images, voice, and screen captures processed seamlessly

Quick Start

Step 1: Backend & Dashboard (Docker):

docker compose up -d --pull always

Step 2: Create an API key in the dashboard (http://localhost:5173) and set as the environmental variable MIRIX_API_KEY.

Step 3: Client (Python, mirix-client, https://pypi.org/project/mirix-client/):

pip install mirix-client

Now you are ready to go! See the example below:

from mirix import MirixClient

client = MirixClient(
    api_key="your-api-key",
    base_url="http://localhost:8531",
)

client.initialize_meta_agent(
    config={
        "llm_config": {
            "model": "gemini-2.0-flash",
            "model_endpoint_type": "google_ai",
            "api_key": "your-api-key-here",
            "model_endpoint": "https://generativelanguage.googleapis.com",
            "context_window": 1_000_000,
        },
        "embedding_config": {
            "embedding_model": "text-embedding-004",
            "embedding_endpoint_type": "google_ai",
            "api_key": "your-api-key-here",
            "embedding_endpoint": "https://generativelanguage.googleapis.com",
            "embedding_dim": 768,
        },
        "meta_agent_config": {
            "agents": [
                {
                    "core_memory_agent": {
                        "blocks": [
                            {"label": "human", "value": ""},
                            {"label": "persona", "value": "I am a helpful assistant."},
                        ]
                    }
                },
                "resource_memory_agent",
                "semantic_memory_agent",
                "episodic_memory_agent",
                "procedural_memory_agent",
                "knowledge_vault_memory_agent",
            ],
        },
    }
)

client.add(
    user_id="demo-user",
    messages=[
        {"role": "user", "content": [{"type": "text", "text": "The moon now has a president."}]},
        {"role": "assistant", "content": [{"type": "text", "text": "Noted."}]},
    ],
)

memories = client.retrieve_with_conversation(
    user_id="demo-user",
    messages=[
        {"role": "user", "content": [{"type": "text", "text": "What did we discuss on MirixDB in last 4 days?"}]},
    ],
    limit=5,
)
print(memories)

For more API examples, see samples/run_client.py.

License

Mirix is released under the Apache License 2.0. See the LICENSE file for more details.

Contact

For questions, suggestions, or issues, please open an issue on the GitHub repository or contact us at founders@mirix.io

Join Our Community

Connect with other Mirix users, share your thoughts, and get support:

💬 Discord Community

Join our Discord server for real-time discussions, support, and community updates: https://discord.gg/S6CeHNrJ

🎯 Weekly Discussion Sessions

We host weekly discussion sessions where you can:

  • Discuss issues and bugs
  • Share ideas about future directions
  • Get general consultations and support
  • Connect with the development team and community

📅 Schedule: Friday nights, 8-9 PM PST
🔗 Zoom Link: https://ucsd.zoom.us/j/96278791276

📱 WeChat Group

You can add the account ari_asm so that I can add you to the group chat.

Acknowledgement

We would like to thank Letta for open-sourcing their framework, which served as the foundation for the memory system in this project.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jl_ecms_server-0.18.1.tar.gz (521.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

jl_ecms_server-0.18.1-py3-none-any.whl (631.1 kB view details)

Uploaded Python 3

File details

Details for the file jl_ecms_server-0.18.1.tar.gz.

File metadata

  • Download URL: jl_ecms_server-0.18.1.tar.gz
  • Upload date:
  • Size: 521.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for jl_ecms_server-0.18.1.tar.gz
Algorithm Hash digest
SHA256 fb9807a027e7c0a583dfdb3587ba23fed304e8e5945118ff3f416b7d2c7a69e7
MD5 77fbcb9170e23f63fe614142b4238b37
BLAKE2b-256 cf8b5cf0ad26a2a05162b64f779cf978d95e4b32095d900b41ac2c541b8655cb

See more details on using hashes here.

File details

Details for the file jl_ecms_server-0.18.1-py3-none-any.whl.

File metadata

  • Download URL: jl_ecms_server-0.18.1-py3-none-any.whl
  • Upload date:
  • Size: 631.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for jl_ecms_server-0.18.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2d3dc7573193e03737960765a5b9d281f8f031921827665d812bfc4c4d486b6a
MD5 631680aa6cebce9a2489b2ce66713107
BLAKE2b-256 757d80bae3807b1cfc4c0f8f11fcc76e0631939b982a115db24a5a289d4e8f24

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page